A New Approach to Electricity Market Clearing With Uniform Purchase Price and Curtailable Block Orders

The European market clearing problem is characterized by a set of heterogeneous orders and rules that force the implementation of heuristic and iterative solving methods. In particular, curtailable block orders and the uniform purchase price (UPP) pose serious difficulties. A block is an order that spans over multiple hours, and can be either fully accepted or fully rejected. The UPP prescribes that all consumers pay a common price, i.e., the UPP, in all the zones, while producers receive zonal prices, which can differ from one zone to another. The market clearing problem in the presence of both the UPP and block orders is a major open issue in the European context. The UPP scheme leads to a non-linear optimization problem involving both primal and dual variables, whereas block orders introduce multi-temporal constraints and binary variables into the problem. As a consequence, the market clearing problem in the presence of both blocks and the UPP can be regarded as a non-linear integer programming problem involving both primal and dual variables with complementary and multi-temporal constraints. The aim of this paper is to present a non-iterative and heuristic-free approach for solving the market clearing problem in the presence of both curtailable block orders and the UPP. The solution is exact, with no approximation up to the level of resolution of current market data. By resorting to an equivalent UPP formulation, the proposed approach results in a mixed-integer linear program, which is built starting from a non-linear integer bilevel programming problem. Numerical results using real market data are reported to show the effectiveness of the proposed approach. The model has been implemented in Python, and the code is freely available on a public repository.Comment: 15 pages, 7 figure

A Community Microgrid Architecture with an Internal Local Market

This work fits in the context of community microgrids, where members of a community can exchange energy and services among themselves, without going through the usual channels of the public electricity grid. We introduce and analyze a framework to operate a community microgrid, and to share the resulting revenues and costs among its members. A market-oriented pricing of energy exchanges within the community is obtained by implementing an internal local market based on the marginal pricing scheme. The market aims at maximizing the social welfare of the community, thanks to the more efficient allocation of resources, the reduction of the peak power to be paid, and the increased amount of reserve, achieved at an aggregate level. A community microgrid operator, acting as a benevolent planner, redistributes revenues and costs among the members, in such a way that the solution achieved by each member within the community is not worse than the solution it would achieve by acting individually. In this way, each member is incentivized to participate in the community on a voluntary basis. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator. Numerical results obtained on a real test case implemented in Belgium show around 54% cost savings on a yearly scale for the community, as compared to the case when its members act individually.Comment: 16 pages, 15 figure

Multiscale design for system-wide peer-to-peer energy trading

The integration of renewable generation and the electrification of heating and transportation are critical for the sustainable energy transition toward net-zero greenhouse gas emissions. These changes require the large-scale adoption of distributed energy resources (DERs). Peer-to-peer (P2P) energy trading has gained attention as a new approach for incentivizing the uptake and coordination of DERs, with advantages for computational scalability, prosumer autonomy, and market competitiveness. However, major unresolved challenges remain for scaling out P2P trading, including enforcing network constraints, managing uncertainty, and mediating transmission and distribution conflicts. Here, we propose a novel multiscale design framework for P2P trading, with inter-platform coordination mechanisms to align local transactions with system-level requirements, and analytical tools to enhance long-term planning and investment decisions by accounting for forecast real-time operation. By integrating P2P trading into planning and operation across spatial and temporal scales, the adoption of large-scale DERs is tenable and can create economic, environmental, and social co-benefits

Ex-ante dynamic network tariffs for transmission cost recovery

This paper proposes a novel tariff scheme and a new optimization framework in order to address the recovery of fixed investment costs in transmission network planning, particularly against rising demand elasticity. At the moment, ex-post network tariffs are utilized in addition to congestion revenues to fully recover network costs, which often leads to over/under fixed cost recovery, thus increasing the investment risk. Furthermore, in the case of agents with elastic market curves, ex-post tariffs can cause several inefficiencies, such as mistrustful bidding to exploit ex-post schemes, imperfect information in applied costs and cleared quantities, and negative surplus for marginal generators and consumers. These problems are exacerbated by the increasing price-elasticity of demand, caused for example by the diffusion of demand response technologies. To address these issues, we design a dynamic ex-ante tariff scheme that explicitly accounts for the effect of tariffs in the longterm network planning problem and in the underlying market clearing process. Using linearization techniques and a novel reformulation of the congestion rent, the long-term network planning problem is reformulated as a single mixed-integer linear problem which returns the combined optimal values of network expansion and associated tariffs, while accounting for price-elastic agents and lumpy investments. The advantages of the proposed approach in terms of cost recovery, market equilibrium and increased social welfare are discussed qualitatively and are validated in numerical case studies

Stacking Revenues from Flexible DERs in Multi-Scale Markets using Tri-Level Optimization

Rapid proliferation of flexible Distributed Energy Resources (DERs) as a result of Net Zero Emissions objectives entails a profound shift in the paradigm of local and national energy systems. Currently, DERs' simultaneous participation in multiple markets is generally restricted, which undermines their profitability. With the aim of increasing the number of business cases for them, a tri-level optimization problem that seeks the maximisation of revenues from DERs is proposed. The optimization problem considers simultaneous participation of different flexible DERs, such as, Electric Vehicles (EVs), Battery Energy Storage Systems (BESSs) and Heating, Ventilation and Air Conditioning (HVACs), in national and local markets. Markets are cleared sequentially, and the model is recast into a tractable single-level problem using its dual formulation and strong duality condition. Results from a case study based on the IEEE 14 bus transmission network, a realistic distribution network and SimBench dataset demonstrate the effectiveness of the proposed approach in increasing profits compared with a baseline scenario

Towards the Integration of Electricity Markets: System-wide and Local Solutions

The creation of an efficient, sustainable, and resilient energy system is of paramount importance in the European agenda. To reach this goal, the integration of the existing structures, and the proposal of new design paradigms are key factors. The contribution of the thesis goes along this line, by proposing two solutions to foster the electricity market integration. At system-wide level, a novel market clearing approach is proposed, to deal with some of the main issues of the European electricity market. At local level, a novel community microgrid market model is developed, where people can pool their resources, trade in a local electricity market, and provide ancillary services. The European market clearing problem is characterized by a set of heterogeneous orders and rules that force the implementation of heuristic and iterative solving methods. In particular, curtailable block orders and the uniform purchase price pose serious difficulties. A block order spans over multiple hours, and can be either fully accepted or fully rejected. The uniform purchase price prescribes that all consumers pay a common price in all the zones, while producers receive zonal prices, which can differ from one zone to another. The uniform purchase price scheme leads to a non-linear optimization problem involving both primal and dual variables, whereas block orders introduce multi-temporal constraints and binary variables into the problem. The market clearing problem in the presence of both the uniform purchase price and block orders is still an open issue in the European context. To deal with this integration problem, a novel, non-iterative, and heuristic-free approach is proposed, which results in a mixed-integer linear program, built starting from a non-linear integer bilevel program. At local level, the increasing share of renewable energy sources, and the availability of storage systems in distribution grids, pave the way to new market designs that favor the local usage of energy. Community microgrids fit in this context. A community microgrid is a collection of entities that pool their resources to achieve an efficient use of their assets. To foster the integration of entities at local level, a novel market model for community microgrid is proposed. By using the community, participants can match their demand and supply through an internal local market with a significant reduction of the exchanges with the grid. As a consequence, each participant can benefit from a reduction of its energy costs, a drop of the energy peak, and can effectively provide ancillary services to the grid. The proposed community model ensures that no entity is penalized by participating in the community. This requirement is termed Pareto superior condition. The proposed approach is structured as a bilevel model, which is then recast as a single mathematical program by using primal and dual relations. Numerical results and sensitivity analyses are reported to show the effectiveness of the two proposed approaches

Sensitivity Analysis of a Local Market Model for Community Microgrids

peer reviewedA community microgrid is a collection of entities that can share energy among themselves. By using the community, participants can match their demand and supply through an internal local market with a significant reduction of the exchanges with the main grid. As a consequence, each participant can benefit from a reduction of its energy costs, a drop of the energy peak demanded from the main grid, and the new capability to provide energy reserve at aggregate level. In this paper, we analyze how the changes of the community market model parameters can affect both the community as a whole, and the welfare of each participants. The analysis is performed by varying the main drivers of the community market model, which are represented by community and storage tariffs, and storage capacity. The numerical results are obtained by using real data based on the MeryGrid project

An exact solution to the market clearing problem with uniform purchase price

The electricity market clearing process can be affected to varying degrees by norms set by regulators. One possible rule is the Uniform Purchase Price, which is implemented, for example, in the Italian day-ahead market with the name of Prezzo Unico Nazionale, which literally means unique national price. This rule requires that all the consumers pay the same price in all the market zones. On the contrary, producers receive the zonal prices, which may differ from one zone to another. As a consequence, traditional market clearing techniques cannot be employed because of this difference in the paid and received price, and current state-of-the-art methods still rely on heuristic search procedures. Starting from a non-linear mixed integer bilevel formulation of the clearing process in presence of uniform purchase price, this paper shows how to obtain a mixed integer linear programming model, which is computationally tractable and able to solve exactly the uniform purchase price problem. Numerical results are reported by testing the algorithm using real data from the Italian day-ahead market

An insurance mechanism for electricity reliability differentiation under deep decarbonization

Securing an adequate supply of dispatchable resources is critical for keeping a power system reliable under high penetrations of variable generation. Strategic reserves have been used by a range of jurisdictions to procure investment in additional generation reserves given the missing money problem in energy only market designs. Given the growing flexibility and heterogeneity of load enabled by advancements in distributed resource and control technology, strategic reserve procurement needs to be able to reflect the different preferences of energy consumers. To address this challenge this paper develops an insurance risk mechanism for the procurement of strategic reserves that is adapted to a future with variable generation and flexible demand. The proposed design introduces a central insurance scheme with prudential requirements that align diverse consumer reliability preferences with the financial objectives of an insurer-of-last-resort. We illustrate the benefits of the scheme in (i) differentiating load by usage to enable better management of the system during times of extreme scarcity, (ii) incentivizing incremental investment in generation infrastructure that is aligned with consumer reliability preferences and (iii) improving overall reliability outcomes for consumers